{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distance benchmarks from CLASS\n",
    "\n",
    "This note generates the benchmarks for comoving radial distances and distance moduli using CLASS. It uses the benchmark models defined in the CCL paper (CCL1-11, not including CCL6), as well as some additional models with unconventional neutrino configurations.\n",
    "The benchmarks were created with the public version of CLASS (v2.6.3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.interpolate\n",
    "\n",
    "import classy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The set of common cosmological parameters. The standard integration stepsize is insufficient at very low redshift (z=0.01) when agreement with CCL at the 1e-5 level is required. We thus set `back_integration_stepsize` to something lower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_params = {\"output\"    : \"\",\n",
    "                \"T_cmb\"     : 2.725,\n",
    "                \"h\"         : 0.7,\n",
    "                \"Omega_cdm\" : 0.25,\n",
    "                \"Omega_b\"   : 0.05,\n",
    "                \"A_s\"       : 2e-9,\n",
    "                \"n_s\"       : 1.0,\n",
    "                \"back_integration_stepsize\" : 1.0e-3, # Required for agreement at <1e-5 with CCL.\n",
    "               }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a set of models with different curvature and number of neutrinos (both massless and massive)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [(\"flat_nonu\",         {\"Omega_k\"  : 0.0,\n",
    "                                 \"N_ur\"     : 3.0}),\n",
    "          (\"pos_curv_nonu\"    , {\"Omega_k\"  : 0.01,\n",
    "                                 \"N_ur\"     : 3.0}),\n",
    "          (\"neg_curv_nonu\"    , {\"Omega_k\"  : -0.01,\n",
    "                                 \"N_ur\"     : 3.0}),\n",
    "          (\"flat_massnu1\"     , {\"Omega_k\"  : 0.0,\n",
    "                                 \"N_ur\"     : 2.0,         # 1 massive neutrino\n",
    "                                 \"N_ncdm\"   : 1,           # 1 species\n",
    "                                 \"m_ncdm\"   : 0.1}),        # Mass\n",
    "          (\"flat_massnu2\"     , {\"Omega_k\"  : 0.0,\n",
    "                                 \"N_ur\"     : 0.0,         # 3 massive neutrinos\n",
    "                                 \"N_ncdm\"   : 2,           # 2 species\n",
    "                                 \"m_ncdm\"   : \"0.03, 0.1\", # Masses\n",
    "                                 \"deg_ncdm\" : \"2, 1\"}),     # Degeneracy\n",
    "          (\"flat_massnu3\"     , {\"Omega_k\"  : 0.0,\n",
    "                                 \"N_ur\"     : 0.0,               # 3 massive neutrinos\n",
    "                                 \"N_ncdm\"   : 3,                 # 3 species\n",
    "                                 \"m_ncdm\"   : \"0.03, 0.05, 0.1\", # Masses\n",
    "                                 \"deg_ncdm\" : \"1, 1, 1\"}),        # Degeneracy\n",
    "          (\"flat_manynu1\"     , {\"Omega_k\"  : 0.0,\n",
    "                                 \"N_ur\"     : 6.0}),         # 6 massless neutrinos\n",
    "          (\"neg_curv_massnu1\" , {\"Omega_k\"  : -0.01,\n",
    "                                 \"N_ur\"     : 4.0,         # 4 massless neutrinos\n",
    "                                 \"N_ncdm\"   : 2,           # 2 species of massive neutrinos\n",
    "                                 \"m_ncdm\"   : \"0.03, 0.1\", # Masses\n",
    "                                 \"deg_ncdm\" : \"1, 1\"}),     # Degeneracy\n",
    "          (\"pos_curv_manynu1\" , {\"Omega_k\"  : 0.01,\n",
    "                                 \"N_ur\"     : 3.0,               # 3 massless neutrinos\n",
    "                                 \"N_ncdm\"   : 3,                 # 2 species of massive neutrinos\n",
    "                                 \"m_ncdm\"   : \"0.03, 0.05, 0.1\", # Masses\n",
    "                                 \"deg_ncdm\" : \"1, 1, 1\"}),        # Degeneracy\n",
    "          \n",
    "          # For consistency with existing CCL tests\n",
    "          (\"CCL1\" ,             {\"Omega_k\"      : 0.0,\n",
    "                                 \"n_s\"          : 0.96,\n",
    "                                 \"N_ur\"         : 3.046,  \n",
    "                                 \"N_ncdm\"       : 0}),\n",
    "          (\"CCL2\" ,             {\"Omega_k\"      : 0.0,\n",
    "                                 \"Omega_Lambda\" : 0.0,\n",
    "                                 \"w0_fld\"       : -0.9,\n",
    "                                 \"wa_fld\"       : 0.0,\n",
    "                                 \"N_ur\"         : 3.046,  \n",
    "                                 \"N_ncdm\"       : 0}),\n",
    "          (\"CCL3\" ,             {\"Omega_k\"      : 0.0,\n",
    "                                 \"Omega_Lambda\" : 0.0,\n",
    "                                 \"w0_fld\"       : -0.9,\n",
    "                                 \"wa_fld\"       : 0.1,\n",
    "                                 \"N_ur\"         : 3.046,  \n",
    "                                 \"N_ncdm\"       : 0}),\n",
    "          (\"CCL4\" ,             {\"Omega_k\"      : 0.05,\n",
    "                                 \"Omega_Lambda\" : 0.0,\n",
    "                                 \"w0_fld\"       : -0.9,\n",
    "                                 \"wa_fld\"       : 0.1,\n",
    "                                 \"N_ur\"         : 3.046,  \n",
    "                                 \"N_ncdm\"       : 0}),\n",
    "          (\"CCL5\" ,             {\"Omega_k\"      : -0.05,\n",
    "                                 \"Omega_Lambda\" : 0.0,\n",
    "                                 \"w0_fld\"       : -0.9,\n",
    "                                 \"wa_fld\"       : 0.1,\n",
    "                                 \"N_ur\"         : 3.046,  \n",
    "                                 \"N_ncdm\"       : 0}),\n",
    "          \n",
    "          (\"CCL7\" ,             {\"Omega_k\"      : 0.0,\n",
    "                                 \"N_ur\"         : 2.0,  \n",
    "                                 \"N_ncdm\"       : 1,                 \n",
    "                                 \"m_ncdm\"       : 0.04}), \n",
    "          (\"CCL8\" ,             {\"Omega_k\"      : 0.0,\n",
    "                                 \"Omega_Lambda\" : 0.0,\n",
    "                                 \"w0_fld\"       : -0.9,\n",
    "                                 \"wa_fld\"       : 0.0,\n",
    "                                 \"N_ur\"         : 1.0,  \n",
    "                                 \"N_ncdm\"       : 2,                 \n",
    "                                 \"m_ncdm\"       : \"0.05, 0.01\"}),\n",
    "          (\"CCL9\" ,             {\"Omega_k\"      : 0.0,\n",
    "                                 \"Omega_Lambda\" : 0.0,\n",
    "                                 \"w0_fld\"       : -0.9,\n",
    "                                 \"wa_fld\"       : 0.1,\n",
    "                                 \"N_ur\"         : 0.0,  \n",
    "                                 \"N_ncdm\"       : 3,                 \n",
    "                                 \"m_ncdm\"       : \"0.03, 0.02, 0.04\"}),\n",
    "          (\"CCL10\" ,            {\"Omega_k\"      : 0.05,\n",
    "                                 \"Omega_Lambda\" : 0.0,\n",
    "                                 \"w0_fld\"       : -0.9,\n",
    "                                 \"wa_fld\"       : 0.1,\n",
    "                                 \"N_ur\"         : 2.0,  \n",
    "                                 \"N_ncdm\"       : 1,                 \n",
    "                                 \"m_ncdm\"       : 0.05}),\n",
    "          (\"CCL11\" ,            {\"Omega_k\"      : -0.05,\n",
    "                                 \"Omega_Lambda\" : 0.0,\n",
    "                                 \"w0_fld\"       : -0.9,\n",
    "                                 \"wa_fld\"       : 0.1,\n",
    "                                 \"N_ur\"         : 1.0,  \n",
    "                                 \"N_ncdm\"       : 2,                 \n",
    "                                 \"m_ncdm\"       : \"0.03, 0.02\"}), \n",
    "        ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run CLASS and get the comoving distances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing flat_nonu\n",
      "Computing pos_curv_nonu\n",
      "Computing neg_curv_nonu\n",
      "Computing flat_massnu1\n",
      "Computing flat_massnu2\n",
      "Computing flat_massnu3\n",
      "Computing flat_manynu1\n",
      "Computing neg_curv_massnu1\n",
      "Computing pos_curv_manynu1\n",
      "Computing CCL1\n",
      "Computing CCL2\n",
      "Computing CCL3\n",
      "Computing CCL4\n",
      "Computing CCL5\n",
      "Computing CCL7\n",
      "Computing CCL8\n",
      "Computing CCL9\n",
      "Computing CCL10\n",
      "Computing CCL11\n"
     ]
    }
   ],
   "source": [
    "z = np.logspace(-1, 3, 50, endpoint=True)\n",
    "\n",
    "d_ang = collections.OrderedDict()\n",
    "d_ang_direct = collections.OrderedDict()\n",
    "d_co = collections.OrderedDict()\n",
    "\n",
    "z_class = collections.OrderedDict()\n",
    "\n",
    "for (model_name, model) in models:\n",
    "    print(\"Computing {}\".format(model_name))\n",
    "\n",
    "    cosmo = classy.Class()\n",
    "    cosmo.set({**class_params, **model})\n",
    "    cosmo.compute()\n",
    "\n",
    "    z_class[model_name] = cosmo.get_background()[\"z\"][:]\n",
    "    d_co[model_name] = cosmo.get_background()[\"comov. dist.\"][:]\n",
    "    d_ang[model_name] = cosmo.get_background()[\"ang.diam.dist.\"][:]\n",
    "\n",
    "    d_ang_direct[model_name] = np.array([cosmo.angular_distance(x) for x in z])\n",
    "    cosmo.struct_cleanup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the interpolation error in z."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model_name, da in d_ang.items():\n",
    "    intp = scipy.interpolate.InterpolatedUnivariateSpline(z_class[model_name][::-1], da[::-1])\n",
    "    plt.plot(z, intp(z)/d_ang_direct[model_name] - 1, label=model_name)\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.legend(loc=\"upper left\", bbox_to_anchor=(1,1))\n",
    "plt.xlabel(\"z\")\n",
    "plt.ylabel(\"Fractional difference\")\n",
    "_ =plt.title(\"Interpolation error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the angular diameter distance for the different models defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fid_model = \"flat_nonu\"\n",
    "for model_name, da in d_ang.items():\n",
    "    intp1 = scipy.interpolate.InterpolatedUnivariateSpline(z_class[fid_model][::-1], d_ang[fid_model][::-1])\n",
    "    intp2 = scipy.interpolate.InterpolatedUnivariateSpline(z_class[model_name][::-1], da[::-1])\n",
    "    plt.plot(z, intp2(z)/intp1(z)-1, label=model_name)\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.legend(loc=\"upper left\", bbox_to_anchor=(1,1))\n",
    "plt.xlabel(\"z\")\n",
    "plt.ylabel(\"Fractional difference\")\n",
    "_ = plt.title(\"Angular diameter distance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create benchmark files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the benchmark files for the whole redshift range (`allz`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_benchmark_file = \"../chi_class_allz.txt\"\n",
    "dm_benchmark_file = \"../dm_class_allz.txt\"\n",
    "\n",
    "z = np.logspace(-2, 3, 10, endpoint=True)\n",
    "\n",
    "chi = np.zeros((11, len(z)))\n",
    "dm = np.zeros((11, len(z)))\n",
    "\n",
    "chi[0] = z\n",
    "dm[0] = z\n",
    "\n",
    "CCL_paper_models = [\"CCL1\", \"CCL2\", \"CCL3\", \"CCL4\", \"CCL5\",\n",
    "                    \"CCL7\", \"CCL8\", \"CCL9\", \"CCL10\", \"CCL11\"]\n",
    "\n",
    "for i, model in enumerate(CCL_paper_models):\n",
    "    chi_intp = scipy.interpolate.InterpolatedUnivariateSpline(z_class[model][::-1], d_co[model][::-1])\n",
    "    chi[i+1] = chi_intp(z)\n",
    "    \n",
    "    d_ang_intp = scipy.interpolate.InterpolatedUnivariateSpline(z_class[model][::-1], d_ang[model][::-1])\n",
    "    dL = d_ang_intp(z) * (1+z)**2\n",
    "    dm[i+1] = 5*np.log10(dL*1e6) - 5\n",
    "    \n",
    "np.savetxt(chi_benchmark_file, chi.T, header=\"z \" + \"   \".join(CCL_paper_models))\n",
    "np.savetxt(dm_benchmark_file, dm.T, header=\"z \" + \"   \".join(CCL_paper_models))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create benchmarks also using the split between low and high redshift (`lowz` and `hiz`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "z_low = np.array([1.0, 2.0, 3.0, 4.0, 5.0])\n",
    "z_high = np.array([10.0, 20.0, 50.0, 100.0, 200.0, 500.0, 1000.0])\n",
    "\n",
    "chi_low_z_benchmark = np.zeros((len(z_low), 11))\n",
    "chi_low_z_benchmark[:,0] = z_low\n",
    "chi_high_z_benchmark = np.zeros((len(z_high), 11))\n",
    "chi_high_z_benchmark[:,0] = z_high\n",
    "\n",
    "chi_low_z_benchmark_file= \"../chi_class_lowz.txt\"\n",
    "chi_high_z_benchmark_file= \"../chi_class_hiz.txt\"\n",
    "\n",
    "CCL_paper_models = [\"CCL1\", \"CCL2\", \"CCL3\", \"CCL4\", \"CCL5\",\n",
    "                    \"CCL7\", \"CCL8\", \"CCL9\", \"CCL10\", \"CCL11\"]\n",
    "\n",
    "for i, model in enumerate(CCL_paper_models):\n",
    "    intp = scipy.interpolate.InterpolatedUnivariateSpline(z_class[model][::-1], d_co[model][::-1])\n",
    "    chi_low_z_benchmark[:,i+1] = intp(z_low)\n",
    "    chi_high_z_benchmark[:,i+1] = intp(z_high)\n",
    "\n",
    "np.savetxt(chi_low_z_benchmark_file, chi_low_z_benchmark, header=\"z \" + \"   \".join(CCL_paper_models))\n",
    "np.savetxt(chi_high_z_benchmark_file, chi_high_z_benchmark, header=\"z \" + \"   \".join(CCL_paper_models))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the benchmark files for the additional neutrino models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_benchmark_file= \"../chi_class_extra_mnu.txt\"\n",
    "dm_benchmark_file= \"../dm_class_extra_mnu.txt\"\n",
    "z_min = 0.01\n",
    "z_max = 1050.0\n",
    "n_z = 10\n",
    "z = np.logspace(np.log10(z_min), np.log10(z_max), n_z, endpoint=True)\n",
    "\n",
    "chi = np.zeros((10, len(z)))\n",
    "dm = np.zeros((10, len(z)))\n",
    "\n",
    "chi[0] = z\n",
    "dm[0] = z\n",
    "\n",
    "extra_mnu_models = [\"flat_nonu\", \"pos_curv_nonu\", \"neg_curv_nonu\", \n",
    "                    \"flat_massnu1\", \"flat_massnu2\", \"flat_massnu3\", \n",
    "                    \"flat_manynu1\", \"neg_curv_massnu1\", \"pos_curv_manynu1\"]\n",
    "\n",
    "for i, model in enumerate(extra_mnu_models):\n",
    "    chi_intp = scipy.interpolate.InterpolatedUnivariateSpline(z_class[model][::-1], d_co[model][::-1])\n",
    "    chi[i+1] = chi_intp(z)\n",
    "    \n",
    "    d_ang_intp = scipy.interpolate.InterpolatedUnivariateSpline(z_class[model][::-1], d_ang[model][::-1])\n",
    "    dL = d_ang_intp(z) * (1+z)**2\n",
    "    dm[i+1] = 5*np.log10(dL*1e6) - 5\n",
    "    \n",
    "np.savetxt(chi_benchmark_file, chi.T, header=\"z \" + \"   \".join(extra_mnu_models))\n",
    "np.savetxt(dm_benchmark_file, dm.T, header=\"z \" + \"   \".join(extra_mnu_models))"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python [conda env:python3_stack]",
   "language": "python",
   "name": "conda-env-python3_stack-py"
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  "language_info": {
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   "file_extension": ".py",
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